Generalization-based Anonymization
Generalization-Based Anonymization is a data anonymization technique that belongs to the class of privacy-preserving methods by altering original data values, specifically distinguished by replacing specific attribute values with broader, less precise categorical ranges to prevent identity disclosure while maintaining data utility.
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6.3K
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201
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106
Institutions
Publications per year
2017–2026
22
Parents
80
Personalized privacy preservation
Xiaokui Xiao, Yufei Tao · 2006 · 666 citations
Aggregate Query Answering on Anonymized Tables
Qing Zhang, Nick Koudas, Divesh Srivastava et al. · 2007 · 361 citations
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106
Chinese University of Hong Kong
Hong Kong, Hong Kong
5 h-index
10 publications
597 citations
University of Technology Sydney
Sydney, Australia
4 h-index
16 publications
2.1K citations
National University of Singapore
Singapore, Singapore
4 h-index
7 publications
670 citations
AT&T (United States)
Bedminster, United States
3 h-index
4 publications
488 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
3 h-index
3 publications
276 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
3 h-index
7 publications
888 citations

Mohammed V University
Rabat, Morocco
3 h-index
6 publications
104 citations

University of Toronto
Toronto, Canada
3 h-index
3 publications
406 citations
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TKDE · 6 publications · 386 citations
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PVLDB · 5 publications · 721 citations
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2 publications · 88 citations
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TODS · 2 publications · 90 citations
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1 publications · 11 citations
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